Internet of Things (IOT) services – namely sensor-based IS
services facilitated by identification technologies such as barcode,
radio frequency or global satellite communication – provide
new security and privacy challenges in private and business
situations of everyday people. Accordingly, the relevance of privacy
and security has been addressed in prior IS research and, as a
result, design methodologies, guidelines and policies have been
discussed and proposed. However, no empirical instrument has been
developed and successfully tested for the class of IOT services that
identifies critical privacy factors, which predict (1) the
behavioral usage of IOT services and (2) individuals’
willingness to provide personal information in both (1) business
situations and (2) private situations. The contribution of this
paper is therefore to address this lack of research. The proposed
underlying research model is based on utility maximization theory
and integrates theoretical constructs from the Extended Privacy
Calculus Model and the Technology Acceptance Model. This model is
pre-tested with 31 IOT experts by an empirical study. Results
indicate that behavioral intentions to use IOT services are
influenced by various contradicting factors such as perceived
privacy risks and personal interest. Additionally, these factors
depend on the underlying usage situation be it a business or a
private situation. It is further assumed that contextual factors
such as legislation and data security as well as transparency of
information use influence the adoption of IOT services. Accordingly,
further research must focus on a better understanding of these
success factors to increase the adoption of both useful and secure
IOT services in the future.